On Monday, 9 February 2015 21:57:51 UTC, Paul Moore wrote:
> On Friday, 6 February 2015 23:49:51 UTC, Steven D'Aprano wrote:
> > Very nice! Care to share the code?
>
> Will do.
Here's the code I used for the Monopoly calculations.
import numpy as np
def monopoly(samples):
# 2d6 x 3
n
On Friday, 6 February 2015 23:49:51 UTC, Steven D'Aprano wrote:
> > Just a quick status update, in case you're interested. With relatively
> > little work (considering I started not knowing much about numpy) I managed
> > to put together solutions for a couple of my friend's problems which ran
> >
Paul Moore wrote:
>
> Yes. And a number of other variations. None gave anything that seemed to
> relate. It's quite likely though that I'm simply not understanding how
> things like pymc (which came up in the searches) might help me, or how to
> convert my problem into a Monte Carlo integration
Paul Moore wrote:
> On Thursday, 5 February 2015 23:02:42 UTC, Paul Moore wrote:
>> Nice! Thanks both of you. I start to see how to think about these
>> things now. I'll play around with some of the other examples I've got,
>> and see how far I get.
>
> Just a quick status update, in case you'
On Thursday, 5 February 2015 23:02:42 UTC, Paul Moore wrote:
> Nice! Thanks both of you. I start to see how to think about these
> things now. I'll play around with some of the other examples I've got,
> and see how far I get.
Just a quick status update, in case you're interested. With relativel
On Thursday, 5 February 2015 21:01:21 UTC, Ian wrote:
> Building on Rob's example:
>
> def monopoly(throws, per=2, rerolls=3, sides=6):
> all_dice = np.random.randint(1, sides+1, size=(throws, rerolls, per))
> doubles = all_dice[...,0] == all_dice[...,1]
> three_doubles = doubles[:,0]
On Thu, Feb 5, 2015 at 12:25 PM, Paul Moore wrote:
> On Thursday, 5 February 2015 16:57:07 UTC, Rob Gaddi wrote:
>> You don't need the whole scipy stack, numpy will let you do everything
>> you want. The trick to working in numpy is to parallelize your problem;
>> you don't do a thing a thousan
On Thu, 05 Feb 2015 11:25:42 -0800, Paul Moore wrote:
> On Thursday, 5 February 2015 16:57:07 UTC, Rob Gaddi wrote:
>> You don't need the whole scipy stack, numpy will let you do everything
>> you want. The trick to working in numpy is to parallelize your
>> problem;
>> you don't do a thing a t
On Thursday, 5 February 2015 16:57:07 UTC, Rob Gaddi wrote:
> You don't need the whole scipy stack, numpy will let you do everything
> you want. The trick to working in numpy is to parallelize your problem;
> you don't do a thing a thousand times; you do it on a thousand-length
> array. For e
On Thu, 05 Feb 2015 08:20:41 -0800, Paul Moore wrote:
> I'm interested in prototyping a Monte Carlo type simulation algorithm in
> Python. The background is that a friend has written a similar program in
> C++, and I'm interested in seeing if I can achieve something comparable
> in a much better
On Thursday, 5 February 2015 16:28:07 UTC, Joel Goldstick wrote:
> have you googled "python monte carlo"?
Yes. And a number of other variations. None gave anything that seemed to
relate. It's quite likely though that I'm simply not understanding how things
like pymc (which came up in the search
On Thu, Feb 5, 2015 at 11:20 AM, Paul Moore wrote:
> I'm interested in prototyping a Monte Carlo type simulation algorithm in
> Python. The background is that a friend has written a similar program in
> C++, and I'm interested in seeing if I can achieve something comparable in
> a much better lan
I'm interested in prototyping a Monte Carlo type simulation algorithm in
Python. The background is that a friend has written a similar program in C++,
and I'm interested in seeing if I can achieve something comparable in a much
better language :-)
The basic job of the program will be to simulat
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